DBMS > Apache Impala vs. Galaxybase vs. PostgreSQL vs. Spark SQL vs. ToroDB

System Properties Comparison Apache Impala vs. Galaxybase vs. PostgreSQL vs. Spark SQL vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for Hadoop

Scalable, ACID-compliant native distributed parallel graph platform

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Document store

Secondary database models

Document store

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.07
Rank	#377	Overall
	#40	Graph DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/torodb/server

Technical documentation

impala.apache.org/impala-docs.html

www.postgresql.org/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

PostgreSQL Global Development Group

www.postgresql.org/developer

Apache Software Foundation

8Kdata

Initial release

2013

2017

1989

1989: Postgres, 1996: PostgreSQL

2014

2016

Current release

4.1.0, June 2022

Nov 20, November 2021

16.3, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

BSD

Open Source

Apache 2.0

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C and Java

Scala

Java

Server operating systems

Linux

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

yes

Strong typed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard with numerous extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined procedures and functions

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

fine grained access rights according to SQL-standard

Access rights for users and roles

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